Thrombin-derived C-terminal peptides bind and form aggregates with sulfated glycosaminoglycans

Glycosaminoglycans (GAGs) such as heparin and heparan sulfate (HS) play crucial roles in inflammation and wound healing, serving as regulators of growth factors and pro-inflammatory mediators. In this study, we investigated the influence of heparin/HS on thrombin proteolysis and its interaction with the generated 11 kDa thrombin-derived C-terminal peptides (TCPs). Employing various biochemical and biophysical methods, we demonstrated that 11 kDa TCPs aggregate in the presence of GAGs, including heparin, heparan sulfate, and chondroitin sulfate-B. Circular dichroism analysis demonstrated that 11 kDa TCPs, in the presence of GAGs, adopt a β-sheet structure, a finding supported by thioflavin T1 (ThT) fluorescence measurements and visualization of 11 kDa TCP-heparin complexes using transmission electron microscopy (TEM). Furthermore, our investigations revealed a stronger binding affinity between 11 kDa TCPs and GAGs with higher sulfate group contents. Congruently, in silico simulations showed that interactions between 11 kDa TCPs and heparin/HS are predominantly electrostatic in nature. Collectively, our study suggests that 11 kDa TCPs have the capacity to aggregate in the presence of GAGs, shedding light on their potential roles in inflammation and wound healing.


Supplementary Information
Generation of 11 kDa TCPs in whole blood or serum.(A-B) A representative image of SDS-PAGE, followed by western blotting, using specific antibodies against the C-terminal thrombin epitope VFR17 (n = 3).(A) Whole human blood (A) or human serum (B) in the presence of the indicated concentrations of heparin.g-thrombin (g-T) was used as a control.(A) The intensity of the bands at 42 kDa, corresponding to molecular mass of a-thrombin, decreases with increasing concentrations of heparin with no formation of TCPs.(B) The intensity of the bands at 42 kDa, corresponding to molecular mass of a-thrombin, remains similar regardless of the concentration of heparin, with no formation of TCPs.

Figure S2
Proteolysis of α-thrombin in vitro.A representative image of SDS-PAGE, followed by western blotting, using specific antibodies against the C-terminal thrombin epitope VFR17.αthrombin in the presence of heparin, HS-3 or CS-B (100 μg/mL) with and without 2 mM protease inhibitors (PMSF) (n = 3).Proteolysis of α-thrombin stimulated by different GAGs is inhibited by PMSF.

Figure S3
Contact analysis of CG simulations of TCP96 with heparin.The number of contacts made by each residue in TCP96 peptide with heparin from CG simulations were calculated.Average from ten copies of TCP96 and three repeat simulations are shown with the standard deviations depicted as error bars.Distance cut off for contact measurement is 0.6 nm.

Figure S4
Binding of rTCP96 to heparin.A representative image of BN-PAGE, followed by western blotting, using specific antibodies against the C-terminal thrombin epitope VFR17.
Recombinantly produced 11 kDa TCP alone or incubated with different doses of heparin.
Buffer containing different doses of heparin was used as a control.A optimized on the basis of QM and experimental data. 14,16any all-atom (AA) FFs, such as GLYCAM, 17 CHARMM, 16 and GROMOS, 18 can correctly reproduce the conformational properties of GAG sequences and their protein binding properties. 19,20AA studies have enabled, for example, an improved understanding of anomeric effects or torsional preferences of sulfate groups and characterization of critical inter-and intramolecular interactions that affect the flexibility and stability of GAGs. 20,21Despite the high resolution accessible to AA FFs, their application to GAG sequences has typically been limited to, at best, dodecasaccharides and, in many cases, to tetra-or hexasaccharides because of the computational cost associated with obtaining sufficient sampling. 22A solution to this is the development of simplified coarse-grained (CG) models, thereby potentially enabling the study of conformational dynamics of higher-order GAG sequences as observed in nature (i.e., ∼20−200 disaccharides long).
In the CG approach, sets of atoms are grouped together and replaced by "pseudoatomic" particles, which results in a reduced number of degrees of freedom in the system, thereby allowing access to longer simulation and time and length scales at the expense of molecular detail.One of the first CG models for CS and HA was developed by Bathe et al., which could predict the influence of pH and ionic strength on persistence length and end-to-end distances, respectively. 23The model was characterized by three beads representing a monosaccharide with an additional virtual particle representing its center of geometry and charge.In another CG model, Sattelle et al. implemented ring puckering in HP, CS, and DS models to study their 3D shape, bioactivities, and hydrodynamic properties. 24In addition, the chain volume and its rigidity were shown to depend on glucuronic acid, the conformational properties of which could be modulated by the sulphonation state.An AMBER-compatible CG FF developed by Samsonov et al. used unique particle types based on several protein−GAG complexes. 25In their model, the bonded interactions were optimized on the basis of AA MD simulations, while the nonbonded parameters were optimized on the basis of potential of mean force calculations.The resultant CG model reproduced many local and global properties from AA simulations, such as mean distributions of the root-meansquare deviation (RMSD), end-to-end distances, and radii of gyration (R g ).In a CG model by Kolesnikov et al., a polymeric field theoretical approach could reproduce osmotic pressure data and many solution properties in the presence of ions. 26

B
optimized on the basis of QM and experimental data. 14,16any all-atom (AA) FFs, such as GLYCAM, 17 CHARMM, 16 and GROMOS, 18 can correctly reproduce the conformational properties of GAG sequences and their protein binding properties. 19,20AA studies have enabled, for example, an improved understanding of anomeric effects or torsional preferences of sulfate groups and characterization of critical inter-and intramolecular interactions that affect the flexibility and stability of GAGs. 20,21Despite the high resolution accessible to AA FFs, their application to GAG sequences has typically been limited to, at best, dodecasaccharides and, in many cases, to tetra-or hexasaccharides because of the computational cost associated with obtaining sufficient sampling. 22A solution to this is the development of simplified coarse-grained (CG) models, thereby potentially enabling the study of conformational dynamics of higher-order GAG sequences as observed in nature (i.e., ∼20−200 disaccharides long).
In the CG approach, sets of atoms are grouped together and replaced by "pseudoatomic" particles, which results in a reduced number of degrees of freedom in the system, thereby allowing access to longer simulation and time and length scales at the expense of molecular detail.One of the first CG models for CS and HA was developed by Bathe et al., which could predict the influence of pH and ionic strength on persistence length and end-to-end distances, respectively. 23The model was characterized by three beads representing a monosaccharide with an additional virtual particle representing its center of geometry and charge.In another CG model, Sattelle et al. implemented ring puckering in HP, CS, and DS models to study their 3D shape, bioactivities, and hydrodynamic properties. 24In addition, the chain volume and its rigidity were shown to depend on glucuronic acid, the conformational properties of which could be modulated by the sulphonation state.An AMBER-compatible CG FF developed by Samsonov et al. used unique particle types based on several protein−GAG complexes. 25In their model, the bonded interactions were optimized on the basis of AA MD simulations, while the nonbonded parameters were optimized on the basis of potential of mean force calculations.The resultant CG model reproduced many local and global properties from AA simulations, such as mean distributions of the root-meansquare deviation (RMSD), end-to-end distances, and radii of gyration (R g ).In a CG model by Kolesnikov et al., a polymeric field theoretical approach could reproduce osmotic pressure data and many solution properties in the presence of ions. 26optimized on the basis of QM and experimental data. 14,16any all-atom (AA) FFs, such as GLYCAM, 17 CHARMM, 16 and GROMOS, 18 can correctly reproduce the conformational properties of GAG sequences and their protein binding properties. 19,20AA studies have enabled, for example, an improved understanding of anomeric effects or torsional preferences of sulfate groups and characterization of critical inter-and intramolecular interactions that affect the flexibility and stability of GAGs. 20,21Despite the high resolution accessible to AA FFs, their application to GAG sequences has typically been limited to, at best, dodecasaccharides and, in many cases, to tetra-or hexasaccharides because of the computational cost associated with obtaining sufficient sampling. 22A solution to this is the development of simplified coarse-grained (CG) models, thereby potentially enabling the study of conformational dynamics of higher-order GAG sequences as observed in nature (i.e., ∼20−200 disaccharides long).
In the CG approach, sets of atoms are grouped together and replaced by "pseudoatomic" particles, which results in a reduced number of degrees of freedom in the system, thereby allowing access to longer simulation and time and length scales at the expense of molecular detail.One of the first CG models for CS and HA was developed by Bathe et al., which could predict the influence of pH and ionic strength on persistence length and end-to-end distances, respectively. 23The model was characterized by three beads representing a monosaccharide with an additional virtual particle representing its center of geometry and charge.In another CG model, Sattelle et al. implemented ring puckering in HP, CS, and DS models to study their 3D shape, bioactivities, and hydrodynamic properties. 24In addition, the chain volume and its rigidity were shown to depend on glucuronic acid, the conformational properties of which could be modulated by the sulphonation state.An AMBER-compatible CG FF developed by Samsonov et al. used unique particle types based on several protein−GAG complexes. 25In their model, the bonded interactions were optimized on the basis of AA MD simulations, while the nonbonded parameters were optimized on the basis of potential of mean force calculations.The resultant CG model reproduced many local and global properties from AA simulations, such as mean distributions of the root-meansquare deviation (RMSD), end-to-end distances, and radii of gyration (R g ).In a CG model by Kolesnikov et al., a polymeric field theoretical approach could reproduce osmotic pressure data and many solution properties in the presence of ions. 26 Figure S5 Comparison of atomistic and coarse-grained (CG) simulations of TCP96.(A) Overlaid snapshots of TCP96 peptide taken every 100 ns from a 1000 ns simulation using all-atom CHARMM36m forcefield (left), CG MARTINI 2.2 forcefield (middle) and CG MARTINI 3 forcefield (right).Helix 80-96, which forms the putative heparin binding exosite in thrombin, is shown in blue, and the rest of the peptide is in cyan.(B) Average per-residue root means square fluctuation (RMSF) from three independent repeats.Shaded regions indicate standard deviations between repeats.